1. Field of Invention
The present invention relates to an image processing technology, and more particularly to a microscopic image fusion method based on region growing.
2. Description of Related Arts
In the optical microscope imaging system, with the increase of the magnification, the depth of field is getting smaller and smaller. Therefore, only those targets at the focal plane or the vicinity thereof are clearly visible, so that even if it has the simplest structure and the relatively flat three-dimensional depth, it is impossible for the object to completely clearly focus in an image. In practical applications of many fields, it is required that the microscopic imaging should not only have high magnification, but also reach a sufficient depth of field, such as the fiber observation of textile industry and the quality inspection of printed circuit board industry. To resolve the above-mentioned technical problem, it is required that the focal length of the optical microscope should be constantly adjusted for obtaining a series of partially clear images, and then the series of partially clear images are inputted into a computer for fusing to obtain the complete and clear image of an object in the whole space. Therefore, the disadvantage that only one focal plane of an object is clearly visible while directly observing by an optical microscope, and the complete and clear image of the object in the whole space are invisible is overcome.
The existing image fusion method is mainly divided into three categories: the pixel-level image fusion method, the feature-level image fusion method and the decision-level image fusion method. The pixel-level image fusion method fuses on the foundation level. Its main advantage is that the fused image contains as many original data as possible, and the accuracy of the fusion is highest, thereby providing the detail information which other fusion levels cannot provide. In general, it processes pixels in the transform domain by means of the wavelet transform or other transformation methods, thus it has relatively higher fusion complexity. The feature-level image fusion method acquires the information from the pre-processed and feature-extracted original input image, such as the comprehensive treatment of the contour, shape, edge and region information. Generally, its implementation needs more empirical parameters. For variable objects, the instability of theirs feature extraction will bring the deviation to the fusion. The decision-level image fusion method makes the optimal fusion decision according to certain criteria and the feasible degree of every decision. However, due to a variety of decision criteria, it needs manual intervention and is difficult to achieve the automatic fusion.